Engineering Commons^™

Numerical Evaluation Of A Zero Poisson’S Ratio Structure In Μ-3d-Printed Self-Expanding Nitinol Stents, Farhana Yasmin, Ana Vafadar, Majid Tolouei-Rad

Research outputs 2022 to 2026

Stenting is a minimally invasive treatment used in managing peripheral artery disease (PAD). However, clinical challenges persist, including in-stent thrombosis and restenosis, primarily driven by axial foreshortening or elongation and suboptimal balance between radial stiffness and flexibility inherent to conventional stent designs. This study proposes an innovative arrow-shaped geometry exhibiting zero Poisson’s ratio (ZPR) behaviour for 3D-printed self-expanding Nitinol stents. The complete stent deployment process was modelled using finite element analysis (FEA), including radial crimping and subsequent expansion to enable systematic parametric investigation while accounting for µ-3D printing constraints. Response surface methodology (RSM) rigorously evaluated mechanical performance, defining peak stress, …

Table Tennis Training Instrument (Ping Pongers), Derick Alvarado, Peter Sandberg, Andrew Wong

Mechanical Engineering Senior Theses

Table Tennis is an extremely popular sport worldwide, boasting an extensive market. However, it is a sport that generally requires other people to both play and practice with. The goal of this project was to create a machine that would serve table tennis balls for the purpose of individual practice. The machine was meant to fill the niche that other machines of the same type filled by allowing individuals to practice on their own without the assistance of another player or a coach. The main goal of the project was to offer a market competitive device, sharing the same basic …

Inductive Train Transport System, Nicholas Root, Devon Gibb, Ryan Cinarkaya, Davis Letsinger

Mechanical Engineering Senior Theses

Light rail systems can move large numbers of people at relatively high speeds as part of the transit networks critical to dense urban centers, lowering greenhouse gas emissions. However, costs associated with traditional light rail systems are incredibly high and hinder the implementation and expansion of such systems. In this paper, we detail the potential implementation of an inductively-charged, battery-powered light rail system with the potential to lower implementation and expansion costs. This system is compared to existing public transit networks and details of the subsystems required for the successful operation of the planned light rail system are established. As …

Camless Valve Engine, Berkeley Burbank, James Thomas Daniel, Haydn Fischer, Nicos Katigbak, John Holden Kleiner, Martin Raabe

Mechanical Engineering Senior Theses

This project focuses on the design and validation of a camless engine valve system as an  alternative to traditional camshaft-based valvetrain architectures. Conventional engines rely on  fixed camshaft profiles that limit performance and efficiency across varying operating  conditions. The goal of this work is to replace both the camshaft and throttle body with a  pneumatically actuated system capable of independently controlling valve timing, lift, and  duration in real time. By removing the mechanical constraints of a camshaft, the system has the  potential to optimize engine operation across all RPM ranges, improving efficiency and overall  performance without requiring major changes to …

Strategies For Extending The Service Life Of Prestressed Concrete I-Shaped Beams, Sanjoy Kumar Bhowmik

Dissertations

Prestressed concrete (PSC) I-shaped beams are widely used in bridge construction because of their structural efficiency and durability. The service life of these beams is reduced and the maintenance frequency is increased due to the distress during fabrication and subsequent deterioration. Although several mitigation strategies have been proposed and implemented, beam end cracking during fabrication remains a major concern. The causes and mitigation strategies for beam end cracking have been studied for decades, but there have been no comprehensive studies utilizing beam end strains during fabrication and lifting at prefabrication plants under normal operational conditions. Moreover, existing maintenance and repair …

Design And Optimization Of A Bluff Body For Energy Harvesting Of Transverse Galloping Induced By Low-Speed Wind Using Bernstein Polynomial Equations, Youssef Wael Abdelmoneim

Theses and Dissertations

In this thesis, a computational framework is proposed for optimizing the aerodynamic shape of bluff bodies used in galloping-based wind energy harvesters. The system targets low-wind speed environments, where normal wind turbines are not effective, offering a potential alternative to batteries used for powering small electronic devices such as wireless sensors. The design relies on the galloping effect, where airflow around a bluff body induces transverse oscillations that drive an energy conversion mechanism. To generate efficient bluff body geometry, the Class-Shape Transformation (CST) method is used to define a wide range of candidate shapes with minimal design parameters. These shapes …

Capstone Review: Warn Winch Proximity Sensor, Leana Girton

University Honors Theses

This thesis reviews the mechanical engineering capstone project that designed a winch proximity sensor for WARN Industries. WARN is very interested in developing this product for market as a safety and ease-of-use accessory, and working with Portland State University was the first step in this process. A group of four mechanical engineering and four electrical engineering capstone students collaborated to research sensor types, test the selected methods, and design and build a prototype. The final design implements an inductive ring, a mechanical button, and bluetooth signaling. The project succeeded in prototyping a proximity sensor for WARN, who will have access …

Gps Tracking Smart Dash Cam Capstone Review, Simrah Saleem

University Honors Theses

This thesis details our design, implementation, and collaborative development of an intelligent vehicle logging system built on a Raspberry Pi 5. Unlike standard consumer dash cams that act as closed "black boxes," our system uses a dual-camera stereo vision setup integrated with centimeter-level accuracy. While we successfully built a functional Proof of Concept capable of event-triggered recording, dual-monitor visualization, and smart detection and recognition, this paper focuses on our engineering journey and the real-world challenges we faced. Using an Agile framework, we split into three specialized sub-teams to handle hardware, database, and interface design in parallel. This structure created unique …

To Wait Or To Transfer? A Three-Level Optimization Framework For Intermodal Transfer Coordination In First Train Timetabling And Bus Bridging Services Management, Hao Li, Liujiang Kang, Norman Weik, Huijun Sun, Qingying Lai, Zhiguang Cao

Research Collection School Of Computing and Information Systems

This study addresses the integrated optimization of the first train timetabling and bus bridging service design (FTT-BBSD) for morning transfer challenges, two critical but interdependent passenger services in the public transit system. In contrast to most existing studies and conventional approaches, this study explicitly models the influence of passenger path choices and transfer mode selections on FTT-BBSD. Through a novel dual-level network representation that integrates subway and bus systems, we formulate the FTT-BBSD problem as a mixed-integer nonlinear programming model. The model simultaneously determines subway and bus timetables and bridging line deployment to minimize total travel time for all first …

Long-Term Mine Planning: A Survey Of Classical, Hybrid And Artificial Intelligence-Based Methods, Nurul Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or …

Extensive And Intensive Margin Labor Supply On Ride-Sourcing Platforms, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Computing and Information Systems

The rapid expansion of ride-sourcing platforms has enabled freelance drivers to flexibly determine both their participation and working hours. Understanding this flexible labor supply behavior is essential for managing platform capacity and evaluating the impacts of pricing and incentive policies on driver welfare. This study develops a labor supply model in which drivers optimally choose whether to participate (extensive margin) and how long to work (intensive margin) to maximize their utility from consumption and leisure. The model incorporates heterogeneity in drivers’ other income, idle time, and participation costs, allowing us to analytically characterize equilibrium labor supply decisions. The results show …

A Review On Credit Card Electronic Fraud Detection Methodologies, Titilayo Mary Sayikanmi, Ibrahim Adepoju Adeyanju, Bolaji Abigail Omodunbi

Mansoura Engineering Journal

Credit card fraud remains a critical and escalating challenge within the global financial ecosystem, driving substantial annual losses and necessitating the continuous evolution of detection methodologies. This paper presents a systematic literature review, conducted via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, which comprehensively analyzes the state-of-the-art in electronic credit card fraud detection. Through a rigorous examination of 49 high-quality studies, this review maps the methodological evolution from traditional rulebased systems and statistical models to advanced artificial intelligence techniques, including machine learning, deep learning, and graph-based approaches. The analysis reveals that while individual methods possess distinct advantages …

Reliable Offline Evaluation Under Exposure Bias: A Cross-Scale Study Of Counterfactual Estimators In Recommender Systems, Lakshmi Pranathi Vutla

Graduate Masters Theses

Offline evaluation underpins model selection in recommender systems, yet historical interaction logs are shaped by prior recommendation policies. Because users only provide feedback on exposed items, logged data entangles user preferences with exposure mechanisms, leading to exposure bias and potentially misleading model comparisons. Counterfactual estimators such as IPS, SNIPS, CRM, and DR offer principled corrections, but their empirical reliability across datasets and exposure regimes remains insufficiently under- stood. We present a systematic, cross-scale study of counterfactual evaluation in recommender systems. Comparing IPS, SNIPS, CRM, and DR on datasets with randomized exposure (Yahoo! R3, Coat, and KuaiRec), we analyze estimator behavior …

Mechanics And Physical Attributes Of Nature-Based Alterations: Rock Reinforcement And Urban Heat Island Assessment, Mary Chikondi Ngoma

Dissertations

Ground improvement is critical to geotechnical and geo-engineering systems, where modification of the properties of geomaterials (rocks and soils) is required to maintain stability and prevent failure of infrastructure installed within and around them. This need has become increasingly important with rapid urbanization and population growth, which intensify demands on surface and subsurface systems and further challenge the performance of supporting geomaterials. As a result, there is growing interest in nature-based solutions, particularly biologically mediated processes such as biocementation, which can enhance the physical, hydraulic, and mechanical properties of geomaterials while offering environmentally sustainable alternatives to conventional ground improvement techniques. …

Enabling Ml/Ai In 6g And Future Wireless Communication With Privacy Preservation, Mec Offloading And Quantum Computing, Changshi Zhou

Dissertations

The forthcoming sixth-generation (6G) and future wireless networks are envisioned to support an unprecedented range of services, delivering ultra-low latency, massive connectivity, and intelligent real-time responsiveness. These capabilities will enable emerging applications such as extended reality (XR), autonomous vehicles (AVs), industrial robotics, and the Internet of Things (IoT) to reach their full potential. Achieving this vision requires the integration of enabling technologies such as artificial intelligence and machine learning (AI/ML) and quantum computing, which are poised to play central roles in shaping the landscape of wireless communication systems.

In AI-native, data-driven, and computing-centric 6G networks, ML models will be deeply …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Vergence Task-Based Neural Pathways With Binocularly Normal Vision And Comorbid Persistent Post-Concussive Symptoms -Convergence Insufficiency, Ayushi Sangoi

Dissertations

Binocular dysfunctions are more prevalent in the persistent post-concussive symptoms (PPCS) population than in the general population. The most prevalent binocular disorder is convergence insufficiency (CI), affecting 3-17% of the general population and up to 10 times as many people with PPCS. CI makes it difficult to fuse or maintain fusion on targets at near, and its symptoms include double or blurry vision and headaches when performing close-range tasks such as reading, which can exacerbate PPCS symptoms. Given controversy over the subjectivity and effectiveness of diagnostic tools and symptom surveys for both PPCS and CI, understanding why CI has high …

A Generative Ai-Driven Computational Framework For Industry-Scale Discovery Of Novel Battery Materials, Joy Datta

Dissertations

The growing demand for sustainable, high-energy-density electrochemical storage has motivated the exploration of multivalent-ion batteries based on earth-abundant elements such as aluminum, calcium, magnesium, and zinc. While multivalent charge carriers offer higher theoretical energy density than lithium, their practical deployment is hindered by sluggish ion transport, strong ion-host interactions, and structural degradation of electrode materials. Identifying host materials that can reversibly accommodate multivalent ions while maintaining structural integrity remains a fundamental challenge. The dissertation develops a scalable, end-to-end computational framework that integrates density functional theory (DFT), machine learning (ML), and generative artificial intelligence (GenAI) to accelerate the discovery of next-generation …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Significant Crash Characteristics Associated With E-Scooter And E-Bike Crashes, Aimee Jefferson

Dissertations

Micromobility devices—namely e-scooters and e-bikes—have rapidly gained popularity in the United Sates, rising from 35 million annual shared rides in 2017 to over 133 million in 2023 (NACTO 2024). But also increasing is the number of injuries associated with these devices; however, most research emphasizes injury and demographic patterns rather than crash characteristics that would inform prevention strategies. Most existing crash research also relies on small sample sizes, lacks nuance distinguishing between involved parties (motorists, pedestrians, single device), and fails to distinguish between bicycle and micromobility crash patterns despite micromobility devices often being instructed to use conventional bicycle facilities. These …

Glass Transition Temperature Of Plga Nanoparticles And The Application In Drug Delivery, Guangliang Liu

Dissertations

The glass transition temperature (T_g) of poly(D,L-lactic-co-glycolic acid) (PLGA) nanoparticles plays a crucial role in governing molecular mobility, diffusion, and consequently, drug release kinetics. However, the interaction among residual surfactant, drug effect, nanoscale confinement, and release medium on T_g remains insufficiently characterized. This study aims to bridge this gap by correlating the thermal behavior of PLGA nanoparticles with their drug release behavior under physiologically relevant conditions.

In the present study, PLGA nanoparticles were synthesized using both nano-emulsion and surfactant-free nano-precipitation approaches. The influence of residual surfactants - poly(vinyl alcohol) (PVA) and didodecyldimethylammonium bromide (DMAB) - was systematically …

Membrane-Engineered Nanotherapeutic Platforms For Drug Delivery: Hollow Fiber Membrane Synthesis Of Lipid Nanoparticles, Biomimetic Nanocarriers, And Nanobubbles, Zhixiang Liu

Dissertations

Lipid-based nanocarriers have emerged as a cornerstone technology for RNA therapeutics, enabling effective intracellular delivery for applications ranging from vaccination to gene regulation. However, current manufacturing approaches, particularly microfluidic-based platforms, face inherent limitations in scalability, throughput, and structural tunability due to their reliance on confined channel geometries and restricted mixing architectures. Addressing these challenges requires fundamentally new strategies that decouple nanoparticle formation from traditional microscale flow constraints while maintaining precise control over physicochemical properties.

This dissertation presents a comprehensive framework for the design, engineering, and application of advanced lipid-based nanocarriers, centered on a hollow fiber membrane (HFM)—assisted nanopore-mediated assembly platform. …

Transcranial Ac Modulation Of Cerebellar Nuclear Activity In Awake Animals, Nuran Kavakli

Dissertations

Entrainment of cerebellar nuclear (CN) cells via cerebellar transcranial alternating current stimulation (ctACS) has been reported in animals under ketamine/xylazine anesthesia. Our main objective was to demonstrate modulation of CN activity in unanesthetized, freely moving animals using ctACS. Multi-channel carbon-fiber electrodes were implanted into the interpositus nucleus for recording multi-unit (MU) activity, and thin-film electrodes were implanted subcutaneously over the posterior cerebellum for stimulation. A frequency-domain-based metric was developed to quantify modulation from MU signals. The results demonstrated modulation in a wide range of frequencies (4 Hz-300 Hz) as in anesthetized animals. In contrast, the amplitude of the peak in …

Deep Learning Approaches For Ctenophore Identification And Tracking, Anagha Bharadwaj

Theses

Ctenophores are translucent marine organisms with nearly invisible tentacles and pose significant challenges due to their transparent morphology and ambiguous structural features. This research addresses the classification and tracking of these organisms and evaluates the performance of current computer vision models under sparse-data environments.

A dataset from the NJIT Life History Lab consisting of microscopic laboratory videos and photographs of different growth stages is used to train and assess a number of convolutional neural network designs, including VGG16, ResNet, BioCLIP2, YOLO, and DeepLabCut. Additionally, a web-based interface is developed to evaluate expert-labeled ground truth with the model's performance.

The findings …

Polarimetric Terahertz Imaging For The Measurement Of Birefringence In Plastic, Rachel Cohen

Theses

Birefringence offers a promising way to observe stress concentration in materials such as glass and plastic, and thereby to identify weaknesses. Polarimetric imaging can be used to measure the birefringence of material, so long as the material is transparent to the light being used for the imaging. In this research, 2D Terahertz imaging was investigated as a means of measuring the birefringence of plastics that are opaque to visible light but transparent to THz radiation, for the eventual purpose of analyzing the residual stress present. In order to do so, two separate terahertz cameras were characterized for potential use in …

Autonomous Exploration Of An Environment With Static Obstacles Using A Ppo Agent, Brandon Knight

Theses

Research in autonomous exploration has created many effective algorithms that have been tested and proven to work in many different virtual and physical environments. Many optimizations have also been developed to reduce computational effort and increase exploration speed.

However, despite optimizations, these algorithms can still require considerable computational effort and time to explore even small environments. To obtain further improvements in computation and exploration speed, a reinforcement learning agent using actor-critic style proximal policy optimization (PPO) is trained to explore various environments efficiently, then compared to an algorithm using contemporary exploration methods.

Testing is performed in virtual environments with ideal …

Retinomorphic Mid-Wave Infrared In-Sensor Processing Engine: Device-To-Architecture Co-Design, Hemalatha Nagaraju

Theses

Conventional frame-based CMOS image sensors acquire full-frame pixel data at discrete time intervals, resulting in substantial spatial redundancy and loss of temporal information between frames. The repeated conversion and transfer of redundant pixel data increases bandwidth and power consumption in machine vision systems. Retinomorphic sensing architectures address these limitations by enabling programmable, analog-domain processing directly at the sensor interface. A compact behavioral model of the PbSe device is developed in HSPICE based on calibrated TCAD simulation data to capture gate-controlled photocurrent modulation under varying illumination and gate bias conditions. Error analysis is performed to quantify the deviation between TCAD-generated photocurrent …

Machine Learning-Driven Prediction And Mechanistic Insight Into Co2 Adsorption On Biomass-Derived Activated Carbons Using Explainable Ai (Xai), Dalia A. Ali Dr.

Chemical Engineering

To improve CO2 uptake in Biomass-Derived Activated Carbon (BDAC), this study develops a multiscale hybrid digital twin framework. By integrating microscopic descriptors from Density Functional Theory and Molecular Dynamics (DFT/MD) with experimental data from 63 chemically diverse biomass precursors, a Gaussian Process Regression (GPR) model was developed using the Materń 5/2 Automatic Relevance Determination (ARD) kernel. The framework achieved high internal training accuracy (R2 = 0.968) and Root Mean Square Error (RMSE = 0.2552), while providing a realistic generalization baseline across heterogeneous precursors with a 5-fold Cross Validated (CV) R2 of 0.1567 and CV RMSE of 0.283. Explainable Artificial Intelligence …

Ai-Augmented Financial Advisors: Comparing Ai And Human Analyst Investment Recommendations In Agreement, Performance, And Firm Size Effects, Crystal Chen

Honors College Theses

This study examines the level of agreement and performance between artificial intelligence (AI) generated investment recommendations and human analyst recommendations across U.S. publicly traded firms. Using a sample of twelve companies categorized by firm size (large, mid, and small), the study collects buy, hold, or sell recommendations from generative AI systems and human analysts. Agreement between AI-to-AI and AI-to-human recommendations is measured using Cohen’s Kappa agreement. Portfolio performance is evaluated by constructing equal-weighted portfolios for each recommendation source and size category. Risk-adjusted returns are measured using the Sharpe ratio over 1-, 2-, and 3-month periods. Furthermore, the study tests whether …

Omama-Db: The Oregon-Massachusetts Mammography Database, Avanih Kanamarlapudi

Graduate Masters Theses

Public datasets for training AI models in breast cancer screening are limited in size and quality, making it difficult to develop reliable systems. We introduce OMAMA-DB, an extensive publicly available collection of 2D mammograms and 3D tomosynthesis volumes. Starting from 967,991 images, we created a curated set of 231,080 images us ing a multi-stage filtering process that removes missing labels, uncommon dimensions, rare scanner types, duplicate studies, and invalid DICOM files. All 2D images then undergo additional outlier detection using histogram filtering and a variational autoen coder to remove low-quality outliers. OMAMA-DB includes pathology-based cancer labels and automated lesion annotations …